English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The means to obtain the rate constants of a chemical reaction is afundamental open problem in both science and the industry. Traditionaltechniques for finding rate constants require either chemical modifications ofthe reactants or indirect measurements. The rate constant map method is amodern technique to study binding equilibrium and kinetics in chemicalreactions. Finding a rate constant map from biosensor data is an ill-posedinverse problem that is usually solved by regularization. In this work, ratherthan finding a deterministic regularized rate constant map that does notprovide uncertainty quantification of the solution, we develop an adaptivevariational Bayesian approach to estimate the distribution of the rate constantmap, from which some intrinsic properties of a chemical reaction can beexplored, including information about rate constants. Our new approach is morerealistic than the existing approaches used for biosensors and allows us toestimate the dynamics of the interactions, which are usually hidden in adeterministic approximate solution. We verify the performance of the newproposed method by numerical simulations, and compare it with the Markov chainMonte Carlo algorithm. The results illustrate that the variational method canreliably capture the posterior distribution in a computationally efficient way.Finally, the developed method is also tested on the real biosensor data(parathyroid hormone), where we provide two novel analysis tools~-- thethresholding contour map and the high order moment map -- to estimate thenumber of interactions as well as their rate constants.

Estimating the Rate Constant from Biosensor Data via an Adaptive Variational Bayesian Approach